But with over 2 billion people using its platform every month, and sharing over billions of text, image and video messages every single day, Facebook's human moderators have more often than not found themselves unable to effectively control the spread of fake news and offensive content on the social media platform. We are therefore exploring ways to bridge the domain gap between our synthetic engine and real-world distribution of text on images.
The system inputs all this text - in various languages - into a recognition model that has been trained on classifiers to understand the context of the text and the image together. It analyzes images and uses historical data, rather than just the visual profile of the individual characters, to understand the writing. Facebook said that the approach would enable the Rosetta which can recognize any type of words of any length which is even the ones that it was not exposed to during the training phase of development. Which words Facebook is censoring is unclear.
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While Rosetta has been created to scan and read images and video frame, a particularly interesting use case of the technology is in scanning the memes, which appear frequently on the company's two platforms. The overall process involves two steps of detecting a rectangular region that might contain text and then performing text recognition using a convolutional neural network (CNN). The company plans to extend it to yet more areas over time.
"Text extracted from images is being used to improve the relevance and quality of photo search, automatically identifying content that violates our hate-speech policy on the platform in various languages, and improve the accuracy of classification of photos in News Feed to surface more personalised content", the networking giant noted.
At confront esteem, understanding images probably won't appear the most essential issue for AI to illuminate.
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